This is a competitive continuation of our Phase-II project. After successfully fulfilling all of its aims, our framework for optimal multi-surface andor multi-object n-D biomedical image segmentation was further extended, validated, and its practical utility demonstrated in clinical and translational image analysis tasks. This Phase-III proposal will develop several important extensions addressing identified limitations of the current framework and specifically focusing on applicability of the methodology to translational and routine healthcare tasks. Novel methods will be developed for simultaneous segmentation of mutually interacting regions and surfaces, automated design of cost functions from segmentation examples, and overcoming failures of automated techniques in routine diagnostic quality images by allowing limited and highly efficient expert input to guide the image segmentation processes. We hypothesize that advanced graph-based image segmentation algorithms merging machine- learning-derived segmentation parameters and image-specific expert guidance will significantly increase quantitative analysis performance in routinely acquired complex diagnostic-quality medical images across diverse application areas. We propose to: 1) Develop 3D, 4D, and generally n-D approaches for simultaneous segmentation of mutually interacting regions (objects) and surfaces. 2) Develop methods for data-driven automated design of cost functions used for surface-based, region-based, and surface-and-region-based graph search image segmentation. 3) Develop Just-Enough-Interaction (JEI) approaches for efficient real-time medical image segmentation, thus achieving robust clinical applicability of quantitative medical image analysis. 4) Assess performance of all developed methods in translational research settings; determine performance in quantitative medical image analysis and radiation oncology treatment planning workflow. As a result, our project will enable routine quantification and therefore personalized care.